Action-estimating device

ABSTRACT

[Problem]To provide an action-estimating device which is capable of estimating an action of a subject appearing in a plurality of time-series images with high precision.[Solution]Provided is an action-estimating device 1 comprising: an estimating-side obtaining unit 12 for obtaining a plurality of time-series images Y in which a subject Z appears; an estimating-side detecting unit 13 for detecting a plurality of articulations A appearing in each time-series image Y; an estimating-side measuring unit 14 for measuring coordinates of the detected plurality of articulations A in each time-series image Y; an estimating unit 15 for estimating an action of the subject Z based on displacement of the coordinates of the measured plurality of articulations A in the plurality of time-series images Y; and a storing unit 3 for storing a plurality of choices of the action to be estimated. The estimating-side detecting unit 13 further detects a background appeared in each time-series image Y. In order to estimate the action of the subject Z, the estimating unit 15 calculates a probability of each of the plurality of choices based on the displacement of the coordinates of the measured plurality of articulations A in the plurality of time-series images Y, and corrects the calculated probability of each of the plurality of choices based on the detected background.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to an action-estimating device forestimating an action of a subject appearing in a plurality oftime-series images.

BACKGROUND OF THE INVENTION

Conventionally, a device which detects a posture of a human appearing intime-series data based on the articulation of the human appearing intime-series data, and recognizes an action of the human based on thechange of the posture is known (for example, Patent Document 1).

PRIOR ART

Patent Document 1: Japanese Patent Application publication No.2017-228100.

SUMMARY OF INVENTION Problem to be Solved by the Invention

Generally, in an action-estimating, a highly probable choice is selectedfrom among a plurality of choices based on the detected posture.Therefore, precise selection of the choice will lead to anaction-estimating with high accuracy.

In view of the foregoing, it is an object of the invention to provide anaction-estimating device for estimating an action of a subject appearingin a plurality of time-series images with high accuracy.

Means for Solving the Problem

The present invention provides an action-estimating device including: anestimating-side obtaining unit configured to obtain a plurality oftime-series images in which a subject appears; an estimating-sidedetecting unit configured to detect a plurality of articulationsappearing in each time-series image; an estimating-side measuring unitconfigured to measure coordinates of the detected plurality ofarticulations in each time-series image; an estimating unit configuredto estimate an action of the subject based on displacement of thecoordinates of the measured plurality of articulations in the pluralityof time-series images; and a storing unit configured to store aplurality of choices of the action to be estimated. The estimating-sidedetecting unit further detects a background appeared in each time-seriesimage. In order to estimate the action of the subject, the estimatingunit calculates a probability of each of the plurality of choices basedon the displacement of the coordinates of the measured plurality ofarticulations in the plurality of time-series images, and corrects thecalculated probability of each of the plurality of choices based on thedetected background.

With this configuration, it becomes possible to focus on the actionhaving high probability to occur by considering the background.Therefore, an action estimation with high accuracy can be realized.Further, it becomes possible to decrease the probability of action thatis unlikely to occur, while increase the probability of action that islikely to occur. Therefore, an action-estimating with higher accuracy isrealized.

It is preferable that the estimating unit excludes one or more choicesfrom among the plurality of choices based on the detected background inorder to estimate the action of the subject.

With this configuration, since the number of actions, which isultimately presented to the user, decreases, it makes easier for theuser to recognize the estimated action. In addition, since one or morechoices are excluded before calculating the probability of choices, onlythe probability of choices, which was not excluded, can be calculatedefficiently, and then the load on the CPU can be reduced.

It is preferable that the choices whose actions have a relation greaterthan a prescribed value between each other are associated with eachother in the storing unit. When any one of the plurality of choicesassociated with each other was excluded or the probability of any one ofthe plurality of choices associated with each other was decreased, theaction estimating unit increases the probability of the associatedchoice which was not excluded or whose probability was not decreased, inorder to estimate the action of the subject.

With this configuration, in the case of “pitching” and “fall-down” forexample, which have actions look alike part of the way, the probabilityof the action which was not excluded is increased. Therefore, it makespossible to perform an action estimating with higher accuracy.

Another aspect of the present invention provides an action-estimatingprogram installed on a computer storing a plurality of choices of actionto be estimated. The program including: a step for obtaining a pluralityof time-series images in which a subject appears; a step for detecting aplurality of articulations appearing in each time-series image; a stepfor measuring coordinates of the detected plurality of articulations ineach time-series image; a step for estimating an action of the subjectbased on displacement of the coordinates of the measured plurality ofarticulations in the plurality of time-series images; and a step fordetecting a background appeared in each time-series image. In theestimating step, a probability of each of the plurality of choices iscalculated based on the displacement of the coordinates of the measuredplurality of articulations in the plurality of time-series images, andthe calculated probability of each of the plurality of choices iscorrected based on the detected background.

It is preferable that, in the estimating step, one or more choices areexcluded from among the plurality of choices based on the detectedbackground.

It is preferable that the choices whose actions have a relation greaterthan a prescribed value between each other are associated with eachother in the computer. In the estimating step, when any one of theplurality of choices associated with each other was excluded or theprobability of any one of the plurality of choices associated with eachother was decreased, the probability of the associated choice which wasnot excluded or whose probability was not decreased is increased.

Another aspect of the present invention provides an action-estimatingdevice including: an estimating-side obtaining unit configured to obtaina plurality of time-series images in which a subject appears; anestimating-side detecting unit configured to detect a plurality ofarticulations appearing in each time-series image; an estimating-sidemeasuring unit configured to measure coordinates of the detectedplurality of articulations in each time-series image; an estimating unitconfigured to estimate an action of the subject based on displacement ofthe coordinates of the measured plurality of articulations in theplurality of time-series images; a setting unit configured to set apurpose or application for the estimating of the action of the subject;and a storing unit configured to store a plurality of choices of theaction to be estimated. In order to estimate the action of the subject,the estimating unit calculates a probability of each of the plurality ofchoices based on the displacement of the coordinates the measuredplurality of articulations in the plurality of time-series images, andcorrects the calculated probability of each of the plurality of choicesbased on the set purpose or application.

With this configuration, it becomes possible to focus on the actionhaving high probability to occur by considering the application orpurpose. Therefore, an action estimation with high accuracy can berealized. Further, it becomes possible to decrease the probability ofaction that is unlikely to occur, while increase the probability ofaction that is likely to occur. Therefore, an action-estimating withhigher accuracy is realized.

It is preferable that the estimating unit excludes one or more choicesfrom among the plurality of choices based on the set purpose orapplication in order to estimate the action of the subject.

It is preferable that the choices whose actions have a relation greaterthan a prescribed value between each other are associated with eachother in the storing unit. When any one of the plurality of choicesassociated with each other was excluded or the probability of any one ofthe plurality of choices associated with each other was decreased, theaction estimating unit increases the probability of the associatedchoice which was not excluded or whose probability was not decreased, inorder to estimate the action of the subject.

Another aspect of the present invention provides an action-estimatingprogram installed on a computer storing a plurality of choices of actionto be estimated and in which a purpose or application for estimating ofaction of a subject is set including: a step for obtaining a pluralityof time-series images in which the subject appears; a step for detectinga plurality of articulations appearing in each time-series image; a stepfor measuring coordinates of the detected plurality of articulations ineach time-series image; and a step for estimating an action of thesubject based on displacement of the coordinates of the measuredplurality of articulations in the plurality of time-series images. Inthe estimating step, a probability of each of the plurality of choicesis calculated based on the displacement of the coordinates of themeasured plurality of articulations in the plurality of time-seriesimages, and the calculated probability of each of the plurality ofchoices is corrected based on the set purpose or application.

It is preferable that, in the estimating step, one or more choices areexcluded from among the plurality of choices based on the set purpose orapplication.

It is preferable that the choices whose actions have a relation greaterthan a prescribed value between each other are associated with eachother in the computer. In the estimating step, when any one of theplurality of choices associated with each other was excluded or theprobability of any one of the plurality of choices associated with eachother was decreased, the probability of the associated choice which wasnot excluded or whose probability was not decreased is increased.

Effects of the Invention

According to the action-estimating device of the present invention, itbecomes possible to estimate an action of a subject appearing in aplurality of time-series images with high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an explanatory view of a usage state of the action-estimatingdevice according to an embodiment of the present invention.

FIG. 2 is a block diagram of a learning device and the action-estimatingdevice according to the embodiment of the present invention.

FIG. 3 is an explanatory view of an articulation group according to theembodiment of the present invention.

FIG. 4 is an explanatory view of a correction of action choices based ona background according to the embodiment of the present invention.

FIG. 5 is a flowchart of an action-estimating in the action-estimatingdevice according to the embodiment of the present invention.

FIG. 6 is an explanatory view of a usage state of the action-estimatingdevice according to a modification of the present invention.

PREFERRED EMBODIMENTS

An action-estimating device 1 according to a preferred embodiment of thepresent invention will be described below, while referring to FIGS. 1 to5.

As shown in FIG. 1, the action-estimating device 1 is used to estimatean action of a subject Z appearing in a plurality of time-series imagesY (e.g., each frame constituting a video or the like) photographed by aphotographing means X (in this embodiment, for easy understanding, thesubject Z is displayed only on the skeleton). In the action-estimating,information learned by a learning device 2 (see FIG. 2) and stored in astoring unit 3 is referred.

First, the configuration of the learning device 2 is described.

As shown in FIG. 2, the learning device 2 includes a learning-sideidentifier 21, a learning-side obtaining unit 22, a learning-sidedetecting unit 23, a correct-answer obtaining unit 24, a learning-sidemeasuring unit 25, and a learning unit 26.

The learning-side identifier 21 is used to identify a plurality ofarticulations A (in the present embodiment, neck, right elbow, leftelbow, waist, right knee, and left knee) of a subject Z. Thelearning-side identifier 21 stores articulation-identifying informationas references for identifying each articulation A, such as shape,direction, and size. Further, the learning-side identifier 21 alsostores supplemental identifying information as references on various“basic posture” (“walking”, “stand-up” etc.) of a subject Z, “motionrange of each articulation A”, and “distance between articulations A.”

In addition, the learning-side identifier 21 also stores backgroundidentifying information (presence, color, and angle of an object;presence of a person, or the like) as the reference for identifying thebackground (i.e. “hospital room”, “office”, “outside” and the like).

The learning-side obtaining unit 22 obtains a plurality of time-seriesimages Y as video images where actions appeared are known. The pluralityof time-series images Y is inputted by the user of the action-estimatingdevice 1.

The learning-side detecting unit 23 detects a plurality of articulationsA appearing in each time-series image Y. Specifically, the learning-sidedetecting unit 23 detects the parts corresponding to thearticulation-identifying information stored in the learning-sideidentifier 21, using an inference model modeled by CNN (ConvolutionNeural Network). Each of the detected articulations A (A1 to A6 inFIG. 1) is selectably displayed on a display unit (not shown).

The learning-side detecting unit 23 also detects backgrounds appeared ineach time-series image Y. Particularly, the learning-side detecting unit23 detects, in each time-series image Y, the part corresponding to thebackground identifying information stored in the learning-sideidentifier 21.

The correct-answer obtaining unit 24 obtains a correct action(hereinafter referred to as correct-action) of the subject Z appearingin the plurality of time-series images Y, on each articulation Adetected by the learning-side detecting unit 23. The correct-action isinputted by the user of the action-estimating device 1. In particular,as shown in FIG. 1, when the subject Z is falling-down in the pluralityof time-series images Y, the user selects each articulation A on thedisplay unit (not shown) and inputs the correct-action “fall-down.”

In addition, in the present embodiment, the correct-answer obtainingunit 24 also obtains a correct-background appeared in the plurality oftime-series images Y. For example, if the correct-background is a“hospital room”, the user will input a “hospital room” tag. Note thatthe choices of the correct-action and the correct-background are storedin the storing unit 3.

The learning-side measuring unit 25 measures coordinates and depths ofthe plurality of articulations A detected by the learning-side detectingunit 23. This measurement is performed on each time-series image Y.

For example, the coordinate and the depth of the articulation A1 in thetime-series image Y at the time t1 can be expressed such as XA1(t 1),YA1(t 1), ZA1(t 1). The depth is not necessarily expressed using thecoordinate and may be expressed as a relative depth in the plurality oftime-series images Y. The depth may be measured by the known method.However, a depth of each articulation A, which has been inputted inadvance in the correct-answer obtaining unit 24, may also be used. Inthis case, for example, the learning unit 26 (described later) learnslike “When the articulation has this size and angle, the articulation isin XX meters depth.”

The learning unit 26 learns the displacement in the plurality oftime-series images Y of the coordinate and the depth of the whole of theplurality of articulations A belonging to each subject Z. Specifically,the learning unit 26 specifies the plurality of articulations Abelonging to each subject Z selected by the correct-answer obtainingunit 24 as an articulation group B (see FIG. 3), and then, learns thedisplacement in the plurality of time-series images Y of the coordinateand the depth of the whole of the articulation group B.

It is considered to use, as the displacement of the coordinate and thedepth of the whole of the articulation group B, the displacement of thecoordinate and the depth of the center point of coordinates of all thedetected articulations A; or the displacement of the coordinate and thedepth of the center of gravity closely related to the body movement.Both of these may also be used to increase the precision. Thedisplacement of the coordinate and the depth of each articulation A maybe taken into account to increase the precision. Note that thecoordinate and the depth of the center of gravity can be calculatedbased on the coordinate and the depth of each articulation A and theweight of each articulation A (including muscle, fat, etc.). In thiscase, information on the weight of each articulation A will be stored inthe learning-side identifier 21 or the like in advance.

The learning unit 26 learns the said displacement in the plurality oftime-series images Y of the coordinate and the depth of the whole of thearticulation group B, in connection with the correct-action inputted inthe correct-answer obtaining unit 24. For example, when thecorrect-action is “fall forward”, the displacement of the coordinate ofthe whole of the articulation group B is learned as “move downward byfirst distance”, and the displacement of the depth of the whole of thearticulation group B is learned as “move forward by second distance.”

Further, the learning unit 26 learns the background detected by thelearning-side detecting unit 23 (the background identifying information)and the correct-background obtained by the correct-answer obtaining unit24 in association with each other. In this way, it becomes possible toestimate; “The background with such background identifying informationis expected to be a hospital room”; “In the case of such backgroundidentifying information, the probability for the background to be ahospital room is 80%” and so on.

In addition, in the present embodiment, the learning unit 26 determinesthe relation between the correct-action and the correct-background,which are obtained by the correct-answer obtaining unit 24. For example,in the case of the background “hospital room”, “walking” is most commonaction, “fall-down” action occurs occasionally, “run” action rarelyoccurs, and “pitching” action never happens. According to theserelations, when the background is “hospital room”, for example, it isdetermined like; “walk: high”, “fall-down: middle” “run: low” and“pitching: non”. The relations determined as such are stored in thestoring unit 3.

It is preferable that the learning unit 26 learns a large amount ofimages having various viewpoints, other than the plurality oftime-series images Y described above. For example, in the case of“hospital room”, it is considered that a large amount of images like“hospital rooms photographed at different angles”, “hospital roomshaving various colored interiors” and “hospital rooms withpresence/absence of nurses and patients” are collected to be learned bythe learning unit 26.

The storing unit 3 stores various choices of action and background,which the user can select, in the correct-answer obtaining unit 24,other than the above described learning result by the learning unit 26.

Next, the configuration of the action-estimating device 1 will bedescribed as below.

As shown in FIG. 2, the action-estimating device 1 includes anestimating-side identifier 11, an estimating-side obtaining unit 12, anestimating-side detecting unit 13, an estimating-side measuring unit 14,and an estimating unit 15.

The estimating-side identifier 11 is used to identify a plurality ofarticulations A (in the present embodiment, neck, right elbow, leftelbow, waist, right knee, and left knee) of a subject Z. Theestimating-side identifier 11 stores articulation-identifyinginformation as references for identifying each articulation A, such asshape, direction, and size. Further, the estimating-side identifier 11also stores supplemental identifying information as references onvarious “basic posture” (“walking”, “stand-up” etc.) of a subject Z,“motion range of each articulation A”, and “distance betweenarticulations A.” In the present embodiment, the same information as inthe learning-side identifier 21 is stored.

In addition, the estimating-side identifier 11 also stores backgroundidentifying information (presence, color, and angle of an object;presence of a person, or the like) as the reference for identifying thebackground (i.e. “hospital room”, “office”, “outside” and the like). Inthe present embodiment, the same information as in the learning-sideidentifier 21 is stored.

The estimating-side obtaining unit 12 is connected to the photographingmeans X and obtains video images (i.e., a plurality of time-seriesimages Y) taken by the photographing means X. In the present embodiment,a plurality of time-series images Y is obtained in real-time. However,it may be obtained later depending on the intended purpose of theaction-estimating device 1.

The estimating-side detecting unit 13 detects a plurality ofarticulations A appearing in each time-series image Y. Specifically, theestimating-side detecting unit 13 detects the parts corresponding to thearticulation-identifying information stored in the estimating-sideidentifier 11, using an inference model modeled by CNN (ConvolutionNeural Network). When the estimating-side detecting unit 13 detects anarticulation A, it can be considered that a subject Z appears in thetime-series image Y.

The estimating-side detecting unit 13 also detects the backgroundappeared in each of the time-series images Y. In detail, theestimating-side detecting unit 13 detects the parts corresponding to thebackground identifying information stored in the estimating-sideidentifier 11 among each of the time-series images Y. Then, theestimating-side detecting unit 13 determines the background whilereferring to the learning result by the learning unit 26 stored in thestoring unit 3. For example, there presented a “bed” and an “infusion”in FIG. 1, so that it is determined as “the background is a hospitalroom.”

The estimating-side measuring unit 14 measures coordinates and depths ofthe plurality of articulations A detected by the estimating-sidedetecting unit 13. This measurement is performed on each time-seriesimage Y.

For example, the coordinate and the depth of an articulation A1 at thetime t1 in the time-series images Y can be expressed such as XA1 (t 1),YA1 (t 1), ZA1 (t 1). The depth is not necessarily expressed using thecoordinate and may be expressed as a relative depth in the plurality oftime-series images Y. The depth may be measured by the known method.However, it is possible to specify the depth referring to the learningunit 26 when the learning unit 26 has already learned about the depth.

The estimating unit 15 estimates the action of the subject Z, based onthe displacement in the plurality of time-series images Y of thecoordinate and the depth of the whole of the articulation group B.Specifically, the estimating unit 15 selects one or more actions withhigh probability from among various action choices (“fall-down”, “walk”,“running” and “throwing”, etc.), while referring to the learning resultby the learning unit 26 stored in the storing unit 3. Thus, in theaction-estimating device 1, the coordinate and the depth of the whole ofthe articulation group B of each subject Z is inputted in a time-seriesinference model, in which LSTM (Long Short Term Memory) is used, and theaction identifying label such as “walking” and “standing” is outputted.

Here, in this embodiment, the estimating unit 15 also considers thebackground appeared in the time-series images Y in order to estimate theaction of the subject Z. In detail, while referring to the relationbetween the correct-action and the correct-background stored in thestoring unit 3, the estimating unit 15 corrects the probability ofchoices of action, according to the background detected (determined) bythe estimating-side detecting unit 13.

For example, the case where those probabilities become “walk: 65%”,“fall-down: 75%” “run: 45%”, and “pitching: 65%” is considered, as shownin FIG. 4 (a) if the action of subject Z is estimated without takinginto account the background, although the background is “hospital room”actually.

In this case, since “pitching” action is similar to “fall-down” part ofthe way, the above probability of “pitching” action is estimated highly.However, “pitching” is an action of exceedingly rare to occur in a“hospital room.”

Then, in the present embodiment, when those relations are determined as“walk: high”, “fall-down: middle” “run: low”, and “pitching: non” for“background: hospital room”, the probability of the action, which isunlikely to occur in a “hospital room”, is corrected downward like “run:from 45% to 30%”, and “pitching: from 65% to 15%”, as shown in FIG. 4(b). Conversely, the probability of the action, which is likely to occurin a “hospital room”, may be corrected upward like “walk: from 65% to80%”, “fall-down: from 75% to 85%”.

Further, when the probability of the action, which is unlikely to occurin a “hospital room”, is corrected downward and the resultingprobability becomes lower than a prescribed value, the action may beexcluded from the action choices. For example, under the condition “theaction of probability less than 30% is excluded”, those actions “run”and “pitching” are excluded, as shown in FIG. 4 (c).

Also, actions, such as “fall-down” and “pitching”, having a relationgreater than a prescribed value between each other may be associatedwith each other. Then, when any one of the associated actions isexcluded or its probability is decreased, the probability of the actionof the other may be increased. In the example of FIG. 4, as shown in(d), since “pitching” is excluded, the probability of “fall-down” isincreased.

Therefore, since the action-estimating device 1 according to theembodiment considers the background appeared in the time-series images Yin order to estimate the action of subject Z, it becomes possible toperform action estimation with higher accuracy.

Next, while referring to the flowcharts in FIG. 5, “estimating of actionof subject Z” according to the action-estimating device 1 is explained.

First, when a plurality of time-series images Y is obtained by theestimating-side obtaining unit 12 (S1), a plurality of articulations Aand backgrounds appearing in each of the time-series images Y aredetected by the estimating-side detecting unit 13 (S2).

Next, the coordinates and the depths of the plurality of articulations Adetected in S2 are measured by the estimating-side measuring unit 14(S3). This measurement is performed for each time-series image Y.

Next, the action of subject Z is estimated by the estimating unit 15based on the displacement in the plurality of time-series images Y ofthe coordinates and the depths of the plurality of articulations Ameasured in S3 (S4).

Finally, the probability of the estimated action is corrected based onthe detected background (S5).

The action-estimating device 1 having such a configuration, for example,can be used in the below purpose; in a nursing home, theaction-estimating device 1 will always photograph inside the room wherecare-receivers (subject Z) are there. Then, if the case for thosecare-receivers to fall or the like are estimated based on thephotographed images, the action-estimating device 1 will give an alerton that fact to a caregiver.

As described above, the action-estimating device 1 according to theembodiment considers the backgrounds appeared in the time-series imagesY in order to estimate the action of subject Z.

With this configuration, it becomes possible to focus on the actionhaving high probability to occur by considering the background.Therefore, an action estimation with high accuracy can be realized.

Further the action-estimating device 1 according to the presentembodiment, in order to estimate the action of the subject Z, calculatesthe probability of each of the plurality of choices based on themeasured displacement in the plurality of time-series images Y of thecoordinates of the plurality of articulations A, and corrects theprobability of the plurality of calculated choices based on the detectedbackground.

With this configuration, it becomes possible to decrease the probabilityof action that is unlikely to occur, while increase the probability ofaction that is likely to occur. Therefore, an action-estimating withhigher accuracy is realized.

Further, the action-estimating device 1 according to the embodiment, inorder to estimate the action of subject Z, excludes one or more choicesfrom among the plurality of choices, based on the detected background.

With this configuration, since the number of actions, which isultimately presented to the user, decreases, it makes easier for theuser to recognize the estimated action. In addition, since one or morechoices are excluded before calculating the probability of choices, onlythe probability of choices, which was not excluded, can be calculatedefficiently, and then the load on the CPU can be reduced.

Further, in the action-estimating device 1 according to the presentembodiment, the choices whose actions have a relation greater than aprescribed value between each other are associated with each other. Whenany one of the plurality of choices associated with each other wasexcluded or the probability of any one of the plurality of choicesassociated with each other was decreased, the probability of theassociated choice which was not excluded or whose probability was notdecreased is increased in order to estimate the action of the subject Z.

With this configuration, in the case of “pitching” and “fall-down” forexample, which have actions look alike part of the way, the probabilityof the action which was not excluded is increased. Therefore, it makespossible to perform an action estimating with higher accuracy.

While the action-estimating device of the invention has been describedin detail with reference to the preferred embodiment thereof, it wouldbe apparent to those skilled in the art that many modifications andvariations may be made therein without departing from the spirit of theinvention, the scope of which is defined by the attached claims.

For example, in the above-described embodiment, though the background isconsidered in order to estimate the action of the subject Z, applicationor purpose of the action-estimation may also be take into account.

In the case where the purpose is to recognize employees' gesture in anoffice, for example, since those actions “fall-down,” “walking,”“running” and “pitching” are inappropriate, the probabilities of thosechoices are decreased or excluded. On the contrary, those probability ofactions like “move of the arms” or “move of the face” can be consideredto be increased. In this case, as is shown in FIG. 6, a setting unit 16is provided to the action-estimating device 1, and the user will set thepurpose or the application (crime prevention, medical nursing careetc.). The relation between the correct-action and the purpose or theapplication are stored in advance in the storing unit 3. When theestimating unit 15 estimates the action of the subject Z, whilereferring to the said relation, it is possible for the estimating unit15 to correct the probability of the choices of action, based on thepurpose or the application, which is set in the setting unit 16.

Further, in the above embodiment, although the relation between thecorrect-action and the correct-background, which is learned by thelearning unit 26, is stored in the storing unit 3, a set value may alsobe stored in advance in the storing unit 3.

In the above embodiment, the storing unit 3 is arranged separately fromthe action-estimating device 1 and the learning unit 2. However, thestoring unit 3 may be mounted in the action-estimating device 1 side orin the learning unit 2 side.

In the above embodiment, the displacement in the plurality oftime-series images Y of the coordinate and the depth of the articulationgroup B is considered in order to estimate the action of the subject Z.However, the displacement of each articulation A in the plurality oftime-series images Y may be used.

Further, in the above embodiment, the case where the subject Z is ahuman is explained. However, it is also possible to use the device inorder to estimate an animal's action or robot's action. In addition, inthe above embodiment, neck, right elbow, left elbow, waist, right knee,and left knee are employed as a plurality of articulations A. However,it is needless to say that the other articulations or more articulationsA may also be employed.

The present invention is also applied to a program that conducts theprocess of the action-estimating device 1, or to a record mediaaccommodating the content of the program. In the case of record media,the program should be installed on the computer or the like. The recordmedia storing the program may be reusable and not one-time use only. Asreusable record media, for example, CD-ROM may be employed, but therecord media is not limited to this. In addition, it is obvious that aplurality of choices of action to be estimated may be stored in thecomputer later. Similarly, the purpose or application of the estimationof the targeted action may also be set in the computer later.

DESCRIPTION OF THE REFERENCE NUMBER

-   1 Action-estimating device-   2 Learning device-   3 Storing unit-   11 Estimating-side identifier-   12 Estimating-side obtaining unit-   13 Estimating-side detecting unit-   14 Estimating-side measuring unit-   15 Estimating unit-   16 Setting unit-   21 Learning-side identifier-   22 Learning-side obtaining unit-   23 Learning-side detecting unit-   24 Correct-answer obtaining unit-   25 Learning-side measuring unit-   26 Learning unit

1. A non-transitory computer-readable medium storing anaction-estimating program for a computer storing a plurality of choicesof action to be estimated, the action-estimating computer programcomprising: a step for obtaining a plurality of time-series images inwhich a subject appears; a step for detecting a plurality ofarticulations appearing in each time-series image; a step for measuringcoordinates of the detected plurality of articulations in eachtime-series image; a step for estimating an action of the subject basedon displacement of the coordinates of the measured plurality ofarticulations in the plurality of time-series images; and a step fordetecting a background appeared in each time-series image, wherein, inthe estimating step, a probability of each of the plurality of choicesis calculated based on the displacement of the coordinates of themeasured plurality of articulations in the plurality of time-seriesimages, and the calculated probability of each of the plurality ofchoices is corrected based on the detected background.
 2. Theaction-estimating program according to claim 1, wherein, in theestimating step, one or more choices are excluded from among theplurality of choices based on the detected background.
 3. Theaction-estimating program according to claim 1, wherein the choiceswhose actions have a relation greater than a prescribed value betweeneach other are associated with each other in the computer, wherein, inthe estimating step, when any one of the plurality of choices associatedwith each other was excluded or the probability of any one of theplurality of choices associated with each other was decreased, theprobability of the associated choice which was not excluded or whoseprobability was not decreased is increased.
 4. The action-estimatingprogram according to claim 2, wherein the choices whose actions have arelation greater than a prescribed value between each other areassociated with each other in the computer, wherein, in the estimatingstep, when any one of the plurality of choices associated with eachother was excluded or the probability of any one of the plurality ofchoices associated with each other was decreased, the probability of theassociated choice which was not excluded or whose probability was notdecreased is increased.
 5. An action-estimating device comprising: anestimating-side obtaining unit configured to obtain a plurality oftime-series images in which a subject appears; an estimating-sidedetecting unit configured to detect a plurality of articulationsappearing in each time-series image; an estimating-side measuring unitconfigured to measure coordinates of the detected plurality ofarticulations in each time-series image; an estimating unit configuredto estimate an action of the subject based on displacement of thecoordinates of the measured plurality of articulations in the pluralityof time-series images; a setting unit configured to set a purpose orapplication for the estimating of the action of the subject; and astoring unit configured to store a plurality of choices of the action tobe estimated, wherein, in order to estimate the action of the subject,the estimating unit calculates a probability of each of the plurality ofchoices based on the displacement of the coordinates of the measuredplurality of articulations in the plurality of time-series images, andcorrects the calculated probability of each of the plurality of choicesbased on the set purpose or application.
 6. The action-estimating deviceaccording to claim 5, wherein the estimating unit excludes one or morechoices from among the plurality of choices based on the set purpose orapplication in order to estimate the action of the subject.
 7. Theaction-estimating device according to claim 5, wherein the choices whoseactions have a relation greater than a prescribed value between eachother are associated with each other in the storing unit, and wherein,when any one of the plurality of choices associated with each other wasexcluded or the probability of any one of the plurality of choicesassociated with each other was decreased, the action estimating unitincreases the probability of the associated choice which was notexcluded or whose probability was not decreased, in order to estimatethe action of the subject.
 8. The action-estimating device according toclaim 6, wherein the choices whose actions have a relation greater thana prescribed value between each other are associated with each other inthe storing unit, and wherein, when any one of the plurality of choicesassociated with each other was excluded or the probability of any one ofthe plurality of choices associated with each other was decreased, theaction estimating unit increases the probability of the associatedchoice which was not excluded or whose probability was not decreased, inorder to estimate the action of the subject.
 9. A non-transitorycomputer-readable medium storing an action-estimating program for acomputer storing a plurality of choices of action to be estimated and inwhich a purpose or application for estimating of action of a subject isset, the action-estimating computer program comprising: a step forobtaining a plurality of time-series images in which the subjectappears; a step for detecting a plurality of articulations appearing ineach time-series image; a step for measuring coordinates of the detectedplurality of articulations in each time-series image; and a step forestimating an action of the subject based on displacement of thecoordinates of the measured plurality of articulations in the pluralityof time-series images, wherein, in the estimating step, a probability ofeach of the plurality of choices is calculated based on the displacementof the coordinates of the measured plurality of articulations in theplurality of time-series images, and the calculated probability of eachof the plurality of choices is corrected based on the set purpose orapplication.
 10. The non-transitory computer-readable medium accordingto claim 9, wherein, in the estimating step, one or more choices areexcluded from among the plurality of choices based on the set purpose orapplication.
 11. The non-transitory computer-readable medium accordingto claim 9, wherein the choices whose actions have a relation greaterthan a prescribed value between each other are associated with eachother in the computer, wherein, in the estimating step, when any one ofthe plurality of choices associated with each other was excluded or theprobability of any one of the plurality of choices associated with eachother was decreased, the probability of the associated choice which wasnot excluded or whose probability was not decreased is increased. 12.The non-transitory computer-readable medium according to claim 10,wherein the choices whose actions have a relation greater than aprescribed value between each other are associated with each other inthe computer, wherein, in the estimating step, when any one of theplurality of choices associated with each other was excluded or theprobability of any one of the plurality of choices associated with eachother was decreased, the probability of the associated choice which wasnot excluded or whose probability was not decreased is increased.
 13. Anaction-estimating method executed on a computer storing a plurality ofchoices of action to be estimated and in which a purpose or applicationfor estimating of action of a subject is set, the method comprising: astep for obtaining a plurality of time-series images in which thesubject appears; a step for detecting a plurality of articulationsappearing in each time-series image; a step for measuring coordinates ofthe detected plurality of articulations in each time-series image; and astep for estimating an action of the subject based on displacement ofthe coordinates of the measured plurality of articulations in theplurality of time-series images, wherein, in the estimating step, aprobability of each of the plurality of choices is calculated based onthe displacement of the coordinates of the measured plurality ofarticulations in the plurality of time-series images, and the calculatedprobability of each of the plurality of choices is corrected based onthe set purpose or application.
 14. The action-estimating programaccording to claim 13, wherein, in the estimating step, one or morechoices are excluded from among the plurality of choices based on theset purpose or application.
 15. The action-estimating program accordingto claim 13, wherein the choices whose actions have a relation greaterthan a prescribed value between each other are associated with eachother in the computer, wherein, in the estimating step, when any one ofthe plurality of choices associated with each other was excluded or theprobability of any one of the plurality of choices associated with eachother was decreased, the probability of the associated choice which wasnot excluded or whose probability was not decreased is increased. 16.The action-estimating program according to claim 14, wherein the choiceswhose actions have a relation greater than a prescribed value betweeneach other are associated with each other in the computer, wherein, inthe estimating step, when any one of the plurality of choices associatedwith each other was excluded or the probability of any one of theplurality of choices associated with each other was decreased, theprobability of the associated choice which was not excluded or whoseprobability was not decreased is increased.